Lightweighting the prediction process of urban states with parameter sharing and dilated operations
Lightweight and high-precision prediction models for urban states are anticipated to run efficiently on resource-limited devices, serving as key technologies for realizing smart city management. However, many existing models, despite achieving high prediction precision, suffer from overly complex de...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2025-08-01
|
| Series: | International Journal of Digital Earth |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/17538947.2025.2468414 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849224298410016768 |
|---|---|
| author | Peixiao Wang Haolong Yang Hengcai Zhang Shifen Cheng Feng Lu Zeqiang Chen |
| author_facet | Peixiao Wang Haolong Yang Hengcai Zhang Shifen Cheng Feng Lu Zeqiang Chen |
| author_sort | Peixiao Wang |
| collection | DOAJ |
| description | Lightweight and high-precision prediction models for urban states are anticipated to run efficiently on resource-limited devices, serving as key technologies for realizing smart city management. However, many existing models, despite achieving high prediction precision, suffer from overly complex designs, leading to low computational efficiency, a large number of learnable parameters, and difficulty in hyper-parameter calibration. In this study, we present a lightweight parameter-shared dilated convolutional network (PSDCN) to address these challenges. Specifically, we define parameter-shared temporal/graph dilated convolution operators to efficiently and accurately capture spatio-temporal correlations without significantly increasing model's computation time and scale of learnable parameters. Furthermore, we establish mathematical relationships between hyperparameters, significantly reducing their number and simplifying the calibration process. The PSDCN model was validated using PM2.5, traffic, and temperature datasets. The results demonstrated that the PSDCN model simplifies hyperparameter calibration. It also either outperforms or matches the prediction accuracy of nine baselines, while achieving better time efficiency and requiring fewer learnable parameters. |
| format | Article |
| id | doaj-art-70ea24b56b6e4e1db4f4f04afdf6d7fa |
| institution | Kabale University |
| issn | 1753-8947 1753-8955 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | International Journal of Digital Earth |
| spelling | doaj-art-70ea24b56b6e4e1db4f4f04afdf6d7fa2025-08-25T11:31:31ZengTaylor & Francis GroupInternational Journal of Digital Earth1753-89471753-89552025-08-0118110.1080/17538947.2025.2468414Lightweighting the prediction process of urban states with parameter sharing and dilated operationsPeixiao Wang0Haolong Yang1Hengcai Zhang2Shifen Cheng3Feng Lu4Zeqiang Chen5State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing, People’s Republic of ChinaGina Cody School of Engineering and Computer Science, Concordia University, Montreal, CanadaState Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing, People’s Republic of ChinaState Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing, People’s Republic of ChinaState Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing, People’s Republic of ChinaNational Engineering Research Center for Geographic Information System, China University of Geosciences, Wuhan, People’s Republic of ChinaLightweight and high-precision prediction models for urban states are anticipated to run efficiently on resource-limited devices, serving as key technologies for realizing smart city management. However, many existing models, despite achieving high prediction precision, suffer from overly complex designs, leading to low computational efficiency, a large number of learnable parameters, and difficulty in hyper-parameter calibration. In this study, we present a lightweight parameter-shared dilated convolutional network (PSDCN) to address these challenges. Specifically, we define parameter-shared temporal/graph dilated convolution operators to efficiently and accurately capture spatio-temporal correlations without significantly increasing model's computation time and scale of learnable parameters. Furthermore, we establish mathematical relationships between hyperparameters, significantly reducing their number and simplifying the calibration process. The PSDCN model was validated using PM2.5, traffic, and temperature datasets. The results demonstrated that the PSDCN model simplifies hyperparameter calibration. It also either outperforms or matches the prediction accuracy of nine baselines, while achieving better time efficiency and requiring fewer learnable parameters.https://www.tandfonline.com/doi/10.1080/17538947.2025.2468414Urban statesspatio-temporal predictiondilated operationparameter sharinghyper-parameter dependence |
| spellingShingle | Peixiao Wang Haolong Yang Hengcai Zhang Shifen Cheng Feng Lu Zeqiang Chen Lightweighting the prediction process of urban states with parameter sharing and dilated operations International Journal of Digital Earth Urban states spatio-temporal prediction dilated operation parameter sharing hyper-parameter dependence |
| title | Lightweighting the prediction process of urban states with parameter sharing and dilated operations |
| title_full | Lightweighting the prediction process of urban states with parameter sharing and dilated operations |
| title_fullStr | Lightweighting the prediction process of urban states with parameter sharing and dilated operations |
| title_full_unstemmed | Lightweighting the prediction process of urban states with parameter sharing and dilated operations |
| title_short | Lightweighting the prediction process of urban states with parameter sharing and dilated operations |
| title_sort | lightweighting the prediction process of urban states with parameter sharing and dilated operations |
| topic | Urban states spatio-temporal prediction dilated operation parameter sharing hyper-parameter dependence |
| url | https://www.tandfonline.com/doi/10.1080/17538947.2025.2468414 |
| work_keys_str_mv | AT peixiaowang lightweightingthepredictionprocessofurbanstateswithparametersharinganddilatedoperations AT haolongyang lightweightingthepredictionprocessofurbanstateswithparametersharinganddilatedoperations AT hengcaizhang lightweightingthepredictionprocessofurbanstateswithparametersharinganddilatedoperations AT shifencheng lightweightingthepredictionprocessofurbanstateswithparametersharinganddilatedoperations AT fenglu lightweightingthepredictionprocessofurbanstateswithparametersharinganddilatedoperations AT zeqiangchen lightweightingthepredictionprocessofurbanstateswithparametersharinganddilatedoperations |